Visualizing Data

Data Visualization Tips

There are a few basic concepts that can help you generate the best visuals for displaying your data:

• Understand your data.

• Determine what you want to communicate.

• Know your audience.

ggplot2

library(ggplot2)

# library(tidyverse)

Plot Ingredients

• Data
• Mapping: maps variables to plot elements
• Geometrics: points, lines, boxes, histograms, bars, etc.
• Scales: controls the mapping of the values in data space to values in aesthetic space
• Guides: controls how visual properties are mapped back to the data space
  • Labels: axis, legend, titles
• Themes: visual themes for the plot.

The Big 3

Only 3 ingredients are required to make a plot.

1. Data
2. Mapping / Aesthetics
3. A “geom”

?ggplot()
ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame())

1. Data

Always begin with the main function in ggplot2: ggplot

**Data are specified via the “data” argument:

ggplot(data = mydata)

This argument supplies a coordinate system to add layers to.

2. Aesthetics

aes() maps variables from a data set to various elements of a plot

• Discrete values (groups / categories) can have color, shape, linetype, or fill mappings.

• Points can have an additional x and y position mappings.

Mappings go into the aes() function as the 2nd argument in ggplot().

ggplot(data=df, aes(x=V1, y=V2, color=V3))

Any part of the plot related to the data goes in aes()

3. Geoms

-geoms are the type of geometrics in your plot.

Common geoms include:

• geom_boxplot()
• geom_histogram()
• geom_line()
• geom_density()
• geom_bar()
• geom_point()
• ETC

Variation in Design

To build your plots layer by layer, you use a continuous combination of geoms:

ggplot(mydata, aes(x, y)) + geom_point() + geom_line()

PSA:

mydata %>% ggplot(aes(x, y)) + geom_point() + geom_line()

Adding layer by layer:

my_plot <- ggplot(df, aes(x, y))
my_plot <- my_plot + geom_point()
my_plot <- my_plot + geom_line()

Printing Plots

You do not need to create an object for the plot:

ggplot(data=df, aes(x=V1, y=V2, color=V3)) + geom_point()

BUT you can assign your plot to a variable…

my_plot <- ggplot(df, aes(x, y)) +  geom_point()

…and then print / view your plot

my_plot